Abstract
Quality control, mainly focused on the assessment of bubble and foam-related parameters, is critical in carbonated beverages, due to their relationship with the chemical components as well as their influence on sensory characteristics such as aroma release, mouthfeel, and perception of tastes and aromas. Consumer assessment and acceptability of carbonated beverages are mainly based on carbonation, foam, and bubbles, as a flat carbonated beverage is usually perceived as low quality. This review focuses on three beverages: beer, sparkling water, and sparkling wine. It explains the characteristics of foam and bubble formation, and the traditional methods, as well as emerging technologies based on robotics and computer vision, to assess bubble and foam-related parameters. Furthermore, it explores the most common methods and the use of advanced techniques using an artificial intelligence approach to assess sensory descriptors both for descriptive analysis and consumers' acceptability. Emerging technologies, based on the combination of robotics, computer vision, and machine learning as an approach to artificial intelligence, have been developed and applied for the assessment of beer and, to a lesser extent, sparkling wine. This, has the objective of assessing the final products quality using more reliable, accurate, affordable, and less time-consuming methods. However, despite carbonated water being an important product, due to its increasing consumption, more research needs to focus on exploring more efficient, repeatable, and accurate methods to assess carbonation and bubble size, distribution and dynamics.